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venv/Lib/site-packages/langchain_community/retrievers/arxiv.py

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+from typing import List
+
+from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain_core.documents import Document
+from langchain_core.retrievers import BaseRetriever
+
+from langchain_community.utilities.arxiv import ArxivAPIWrapper
+
+
+class ArxivRetriever(BaseRetriever, ArxivAPIWrapper):
+    """`Arxiv` retriever.
+
+    Setup:
+        Install ``arxiv``:
+
+        .. code-block:: bash
+
+            pip install -U arxiv
+
+    Key init args:
+        load_max_docs: int
+            maximum number of documents to load
+        get_ful_documents: bool
+            whether to return full document text or snippets
+
+    Instantiate:
+        .. code-block:: python
+
+            from langchain_community.retrievers import ArxivRetriever
+
+            retriever = ArxivRetriever(
+                load_max_docs=2,
+                get_ful_documents=True,
+            )
+
+    Usage:
+        .. code-block:: python
+
+            docs = retriever.invoke("What is the ImageBind model?")
+            docs[0].metadata
+
+        .. code-block:: none
+
+            {'Entry ID': 'http://arxiv.org/abs/2305.05665v2',
+            'Published': datetime.date(2023, 5, 31),
+            'Title': 'ImageBind: One Embedding Space To Bind Them All',
+            'Authors': 'Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra'}
+
+    Use within a chain:
+        .. code-block:: python
+
+            from langchain_core.output_parsers import StrOutputParser
+            from langchain_core.prompts import ChatPromptTemplate
+            from langchain_core.runnables import RunnablePassthrough
+            from langchain_openai import ChatOpenAI
+
+            prompt = ChatPromptTemplate.from_template(
+                \"\"\"Answer the question based only on the context provided.
+
+            Context: {context}
+
+            Question: {question}\"\"\"
+            )
+
+            llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+
+            def format_docs(docs):
+                return "\\n\\n".join(doc.page_content for doc in docs)
+
+            chain = (
+                {"context": retriever | format_docs, "question": RunnablePassthrough()}
+                | prompt
+                | llm
+                | StrOutputParser()
+            )
+
+            chain.invoke("What is the ImageBind model?")
+
+        .. code-block:: none
+
+             'The ImageBind model is an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data...'
+    """  # noqa: E501
+
+    get_full_documents: bool = False
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        if self.get_full_documents:
+            return self.load(query=query)
+        else:
+            return self.get_summaries_as_docs(query)